Extracting street centerlines from probe traffic data is a difficult task. One approach used to accomplish this task is to count the number of probe points per “pixel” (square region) and skeletonize this rasterized surface to thin avenues of travel down to a single pixel width, as these single pixel width lines can be easily vectorized. Skeletonization is an established image processing routine but involves a technique to turn a color or grayscale image into a binary image (all pixels either black or white). In this use of skeletonization, white pixels represent streets while black pixels represent non-street locations (referred to in image processing as segmentation).
Thresholding is a common technique used to determine whether pixels are black or white. However, the use of a threshold for real-world probe data does not generally produce acceptable results, because of the drastic differences in the magnitude of probe traffic. For example, the noise around freeways may create larger amounts of probe data than the actual desired traffic on nearby residential streets. Manually varying the threshold to avoid this problem would result in an undesirable loss of automatization.